Fall 2016 Syllabus
This syllabus is subject to change. Note that unreleased project out and due dates are just guesses and will likely change somewhat.
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| Day | Topic | Optional Reading | Slides | Video | Assignment | Due |
| Thu 8/25 | Introduction to AI |
Ch. 1 |
pdf / pptx | lecture1 | Math Self-Diagnostic P0: Tutorial |
(ungraded) W 8/31 5pm |
| Tu 8/30 | Agents and Search |
Ch. 3.1-4 (2e: Ch. 3) |
pdf / pptx | lecture2 | M 9/5 11:59pm | |
| Th 9/1 | A* Search and Heuristics | Ch. 3.5-6 (2e: Ch. 4.1-2) | pdf / pptx | lecture3 |
F 9/16 5pm Su 9/18 11:59pm |
|
| Tu 9/6 | Constraint Satisfaction Problems |
Ch. 6.1 (2e: Ch. 5.1) |
pdf / pptx | lecture4 |
M 9/12 11:59pm |
|
| Th 9/8 | CSPs II | Ch. 6.2-5 (2e: Ch. 5.2-4) | pdf / pptx | lecture5 | ||
| Tu 9/13 | Game Trees: Minimax |
Ch. 5.2-5 (2e: Ch. 6.2-5) |
pdf / pptx | lecture6 | M 9/19 11:59pm | |
| Th 9/15 | Game Trees: Expectimax; Utilities | Ch. 5.2-5 (2e: Ch. 6.2-5) | pdf / pptx | lecture7 |
F 9/30 5pm Sun 10/16 11:59pm |
|
| Tu 9/20 | Markov Decision Processes |
Ch. 16.1-3 |
pdf / pptx | lecture8 | M 9/26 11:59pm | |
| Th 9/22 | Markov Decision Processes II | Sutton and Barto Ch. 3-4 | pdf / pptx | lecture9 | ||
| Tu 9/27 | Reinforcement Learning |
Ch. 17.1-3, S&B Ch. 6.1,2,5 |
pdf / pptx | lecture10 | M 10/3 11:59pm | |
| Th 9/29 | Reinforcement Learning II | pdf / pptx | lecture11 | F 10/14 5pm | ||
| Tu 10/4 | Probability | Ch. 13.1-5 (2e: Ch. 13.1-6) | pdf / pptx | lecture12 | M 10/17 11:59pm | |
| Th 10/6 | MIDTERM (7-9p) | |||||
| Tu 10/11 | Bayes' Nets: Representation |
Ch. 14.1-2,4 |
pdf / pptx | lecture13 | ||
| Th 10/13 | Bayes' Nets: Inference | Ch. 14.3, Jordan 2.1 | pdf / pptx | F 10/28 5pm | ||
| Tu 10/18 | Bayes' Nets: Sampling | Ch. 14.4-5 | pdf / pptx | lecture15 |
W 10/28 11:59pm |
|
| Th 10/20 | Decision Networks / VPI |
Ch. 15.1-3, 6 |
pdf / pptx | lecture16 | ||
| Tu 10/25 | Markov Models and HMMs | Ch. 15.2-5 | pdf / pptx | lecture17 |
M 10/31 11:59pm |
|
|
Th 10/27 |
HMMs and Particle Filtering |
Ch. 15.2,6 |
pdf / pptx | lecture18 | ||
| Tu 11/1 | ML: Naive Bayes | Ch. 15.2,6 | pdf / pptx | lecture19 |
(section 9 / exam-prep 9) |
|
| Th 11/3 | ML: Perceptrons | Ch. 15.2,6 | pdf / pptx | lecture20 | F 11/18 5pm | |
| HW8 |
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| Tu 11/8 | No Lecture | HW9 | M 11/21 11:59pm | |||
| We 11/9 | MIDTERM 2 (7-9p) | |||||
| Th 11/10 | ML: Deep Learning I | pdf / pptx | lecture21 |
Final Contest |
Fri 12/9 11:59pm | |
| Tu 11/15 | ML: Deep Learning II | pdf / pptx |
lecture22 (first 25 min) lecture22 (last 40 min) |
W 11/30 11:59pm |
||
| Th 11/17 | Alyosha Guest Lecture | Lecture23 |
P6: Classification |
M 12/5 5:00pm | ||
| Tu 11/22 | Advanced Topics: Speech Recognition | pdf / pptx | Lecture24 | |||
| Th 11/24 | Thanksgiving | |||||
| Tu 11/29 | Advanced Topics: Robotics | pdf / pptx | lecture25 | (section 11 / exam-prep 11) | ||
| Th 12/1 | Advanced Topics / Final Contest | pdf / pptx | lecture26 | |||
| TBD | FINAL EXAM | |||||